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Digital Modulation Recognition in OFDM Systems Using Support Vector Machine Classifier

M. E. Keshk, M. Abd El-Naby, S. Elrabie, M.I. Dessouky, F. E. Abd El-Samie


Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Orthogonal Frequency Division Multiplexing (OFDM) systems using Discrete Transforms (DT) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. The proposed algorithm uses various DT techniques as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract the features of the modulated signal and Support Vector Machine (SVM) to classify the modulation orders. The proposed algorithm avoids over fitting and local optimal problems that appear in artificial neural networks (ANNs). Simulation results show the classifier to be capable of recognizing the modulation scheme with high accuracy (90-100% when using DWT, DCT and DST for some modulation schemes) over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN) and Rayleigh fading channel, particularly at a low signal to noise ratio (SNR).



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